1. Field of the Invention
The invention relates to a method for customizing searches of the internet. More specifically, the invention relates to a method for searching the internet and providing customized recommendations responsive to a user""s real world activities.
2. Background
The internet is ubiquitous in popular culture. As more and more people go on-line and begin experiencing what the internet has to offer, more and more people are becoming frustrated with the huge amount of data available for consumption via the enormous number of web sites existing in cyberspace. This includes users at home or at work using the internet to pursue hobbies, do homework, do research for school or work projects, etc.
After a user establishes a connection with the internet, the user typically wants to find information of some sort. A common method of finding information on the internet is by using one of the plethora of internet web search engines, e.g., ALTAVISTA.COM, GO.COM, and GOTO.COM. After entering key words describing a concept, thing or event, a multitude of web sites are provided to the user. However, because of the enormous number of web sites that exist in cyberspace, all but the most specialized requests return at least hundreds, typically thousands, and often tens of thousands of web sites. The order in which the web sites are presented is determined by rules at the search engine or randomly. One such rule is based on fees paid by web sites to be listed with the search engine such that the entities paying the larger sums have their web sites displayed on the top of the list provided to the user, e.g., GOTO.COM.
To assist users in beginning to manage the enormous amount of data available on the internet, many web sites provide a rudimentary customization of information for the user. These rudimentary customizations are, however, limited to selection and organization of the information available on the particular web sites and not over the entire internet. For example, shopping web sites allow users to select favorite product areas, choose favorite designers and manufacturers, specify user information such as sizes and colors, etc. (e.g., BLUEFLY.COM). Other examples include news sites which display categories of news based on user specified interest areas and user information such as geographical location. (e.g., MYPAGE.GO.COM).
Internet activity of users is monitored by various companies that track usage patterns of internet web surfers. Web site operators use this information to direct adds to users based on typical web surfing patterns. These advertisements are, thus, responsive to users"" interests as reflected in web site visitations. However, consumers and businesses do not have access to this information.
In the real world, the activities of persons and businesses are also tracked to a limited extent. For example, when a consumer makes purchases at a grocery or drug store, consumers often swipe a personal identification card to obtain discounted prices. Similarly, when purchases are made by consumers and businesses at membership only stores, a membership identification card is presented. In this way, retailers track information about and monitor the buying habits of their customers. However, consumers and businesses do not have access to this information.
The real world of bricks and mortar stores and cyberspace are beginning to overlap. Companies are now producing internet connected cash registers which have instant access to inventory and the company""s web site, including web placed orders. To authorize a credit card transaction, cash register computers connected to the internet obtain automatic authorization of credit card purchases via the internet. In addition, to give users confidence in the security of transactions over the internet and to ease making purchases on the internet, credit card companies have developed credit cards which can be inserted into card readers attached to user""s personal computers which authorize and ease on-line purchases.
Although the internet promises to be pervasive in our society, credit cards already are. Consumers routinely use credit cards to pay for any kind of transaction imaginable, from purchasing groceries, to paying for a dental exam, to buying movie tickets. Businesses also use credit cards for purchasing employee travel, office supplies, office equipment, etc. When credit card transactions are transmitted to the credit card issuer, limited information such as the total amount of the transaction and the name of the entity to be credited are maintained. In this way, general buying habits are monitored and maintained by credit card companies and are offered for sale. However, consumers and businesses do not have access to this information.
Consumers and groups of consumers have not benefited from and do not have access to the plethora of information maintained about them by retailers, credit card companies, internet tracking companies, and others. Similarly, businesses have not benefited from and do not have access to the plethora of information maintained about them and their employees by retailers, credit card companies, internet tracking companies, and others.
A method for providing recommendations to a user based on user activity. A plurality of activity data tracking a plurality of activities of a user is obtained. The activity data may be obtained over a wide area network such as the internet or downloaded from a data card which stores activity data whenever the user participates in an activity. The activity data is either stored on the data card or transmitted over the network whenever the user uses a card when participating in any activity such as when making a purchase of goods, paying for services, watching television, etc. The activity data is processed to identify a plurality of user patterns. The user patterns are used to form a user profile and may include user habit data. Recommendations specific to the user based on the user patterns are then created for and provided to the user. The recommendations are provided to a user when the user logs onto a computer network such as the internet. The recommendations may also be provided by electronic mail, electronic pager or other methods. The recommendations are provided by various data analysis techniques including rule based inference engines and other forms of artificial intelligence.